Abstract The rapid development of Cloud-IoT computing environments enables intelligent services, but raises serious privacy and trust challenges due to massive distributed data generation. This paper proposes a verifiable multi-layer privacy-preserving Cloud-IoT computing framework that integrates differential privacy, secret sharing, and gradient masking within a cloud-edge-end collaborative architecture. An adaptive differential privacy mechanism dynamically adjusts noise intensity according to data sensitivity and training dynamics, while edge intelligence supports efficient pre-aggregation and privacy measurement. Extensive experiments in a real Cloud-IoT environment with 200 terminal devices demonstrate that the proposed framework improves model convergence speed by 37.8%, reduces communication overhead by 89.1%, and decreases privacy leakage risk by up to 82.9% compared with the DP-FedAvg and SecAgg baselines. Meanwhile, it maintains 91.3% model accuracy, suppresses membership inference attack success rates to 52.1%, which is close to the random-guessing baseline (50%), indicating that the attacker’s advantage is largely suppressed. The framework introduces only 3.2% additional verification overhead through a lightweight zero-knowledge proof mechanism. These results indicate that the proposed approach effectively balances privacy protection, verifiability, and system efficiency, providing a practical solution for large-scale Cloud-IoT applications in privacy-sensitive domains such as healthcare and financial services.
Mnemosyne: Post-Quantum Distributed AI Infrastructure via Physical Security Barriers, Speculative Consensus, and Proof-of-Useful-Work on Heterogeneous Edge Networks Overview Mnemosyne is a theoretical framework and system design for running large language model (LLM) inference on heterogeneous edge devices — from Raspberry Pi to high-end workstations — with privacy guarantees that remain valid even after quantum computers break all existing cryptographic assumptions. This paper presents 14 original theorems and 3 new network protocols, spanning five interconnected layers: Layer 1 — OS-Level Memory Management (Ch. 3.1)Formalizes a 6-tuple system model covering semantic-aware LRU page replacement, zero-copy mmap, and delta encoding. Defines four system invariants verified via TLA+ specification. Layer 2 — Information-Theoretic Compression (Ch. 3.2–3.4, Theorems 5.1–5.3)Proves that delta encoding of LLM embedding sequences achieves a lower differential entropy bound when adjacent vector correlation ρ > 0.5. Static analysis of LLaMA-2-7B confirms ρ ≈ 0.85, yielding a theoretical compression gain of ~10.88× over FP16. Full invertibility and floating-point stability bounds are proven. Layer 3 — Thermodynamic Privacy Guarantee (Ch. 5–6, Theorems 7.1–8.4)The core contribution of this paper. Mnemosyne's privacy guarantee is grounded in Landauer's Principle and the Second Law of Thermodynamics, not computational hardness assumptions. Theorem 8.3 proves that exhaustive reconstruction of compressed embeddings requires a minimum energy of 10^{38,778} joules — approximately 10^{38,709}× the total energy of the observable universe. This makes Mnemosyne the first federated learning system, to our knowledge, whose privacy bound is elevated to the level of a physical law. The system is formally characterized as an Inverse Maxwell's Demon: it actively amplifies entropy to make information reconstruction thermodynamically infeasible, rather than computationally difficult. Layer 4 — Distributed Consensus (Ch. 7, Theorems 9.1–9.2)Proves the existence and feasibility of a Global Decentralized Compute Grid (GDCG) across heterogeneous hardware. Introduces a Byzantine Fault-Tolerant (BFT) extension of the MESI protocol with three new states (RS, PF, EC), enabling zero-copy memory sharing across devices. Theorem 9.2 proves that the system-recognized Modified state exists in at most one node among all nodes (including Byzantine nodes) at any time. Layer 5 — Economic Incentive Model (Ch. 7.4, Protocol 2)Defines Proof-of-Useful-Work (PoUW), a five-dimensional incentive function replacing wasteful Proof-of-Work mining with verifiable AI inference contributions. Projected annual reward: USD 100–500 per edge device. Key Contributions First federated learning system with privacy guarantee grounded in the Second Law of Thermodynamics 14 original theorems spanning information theory, thermodynamics, distributed systems, and formal verification 3 new network protocols (BFT-MESI extension, PoUW, QClock consensus) Formal verification via TLA+ and Z3 SMT Solver Minimum hardware requirement: 8 GB RAM (ARM Cortex-A76 class), enabling LLaMA-2-7B inference on commodity edge devices Keywords Edge AI · LLM Inference · Landauer's Principle · Post-Quantum Security · Delta Encoding · Product Quantization · Byzantine Fault Tolerance · Distributed Systems · Information Thermodynamics · Maxwell's Demon · Proof-of-Useful-Work · Federated Learning
Technical Whitepaper (Genesis v1.0) This paper introduces the CLR Protocol, a Layer-1 distributed ledger designed to solve the state-bloat and inflation problems inherent in current Metaverse architectures. Unlike traditional blockchains that rely on arbitrary hashing for address generation, the CLR Chain utilizes a deterministic, bijective mapping of the 24-bit sRGB Color Spectrum to creating a finite, immutable spatial coordinate system. Key Innovations: Topological Hard Cap: The land supply is strictly bounded by the mathematical limit of the 24-bit integer space (16,777,216 unique volumetric units). O(1) Spatial Indexing: Implementation of a Direct Address Table structure replacing traditional B-Tree spatial queries. Proof-of-Spatial-Activity (PoSA): A hybrid consensus mechanism combining liquidity staking with active spatial verification challenges. Entropy Economics: An algorithmic decay function preventing passive rent-seeking and enforcing monetary velocity. This architecture establishes a "Digital Physics" layer where the visual identity of an asset (its color) acts as its cryptographic address, eliminating the abstraction gap between the user interface and the database logic.
We propose Proof of Witness (PoWit), a novel consensus mechanism for digital currency that replaces energy-intensive mining and capital-based staking with independent third-party witness verification. In PoWit, each transaction requires cryptographic signatures from three parties: sender, receiver, and a randomly selected witness. The witness validates the sender’s balance and transaction history before signing, eliminating the need for global consensus while maintaining security guarantees. Our simulation with 10,000 users demonstrates 100% double-spending prevention (n = 10, 000, 99% CI [99.93%, 100%]), 113.9 transactions per second, and complete chain integrity. The non-selective witness assignment achieves theoretical randomness with only 0.27% deviation, making collusion attacks impractical. PoWit offers a sustainable alternative to Proof of Work and Proof of Stake, with significantly lower energy consumption and fairer participation model.
KrishnaBhargavi Yerraganti, D. Balasubrahmanyam, G. Venkat Vamsi, Raja Bhaiya Rajbhar · 5 authors
Centralized traditional social media platforms are controlled by entities that monetize user data, restrict content visibility, and erode privacy. In contrast, current blockchain-based social media platforms are decentralized but inefficient in moderating content, resulting in the proliferation of misinformation, objectionable content, and security threats. This paper aims to bridge this gap by developing a decentralized social media platform that ensures user privacy, content moderation, and scalability without relying on centralized control. To achieve this, the system takes advantage of IPFS for decentralized file collection and Ethereum-compatible smart contracts for authentication and content verification. A major innovation of this platform is the use of an on-device-operated Natural Language Processing (NLP) model for material filtering and moderation at the user level, ensuring that no user data is collected or centrally processed. The platform will support safe text messaging, image sharing (public/private accounts), and an explore page for the discovery of public content, while all users give full control over their data. The expected results are a scalable, censorship-resistant and privacy-centric social media network, where users maintain their content ownership while AI ensures a safe digital environment.
Under the background of the deepening of digital China strategy and the diversification of public archives demand, the insufficient sharing and inefficient utilization of archives information resources have become a prominent bottleneck restricting the release of archives value. This paper systematically combs the policy evolution, platform practice and technology application status of file sharing in China, and finds that the lack of metadata standards, vague boundaries of powers and responsibilities, weak security prevention and control, and the interweaving of the concepts of "valuing custody and neglecting utilization" have formed systematic obstacles such as poor cross-domain circulation and mismatch between supply and demand. Therefore, this paper proposes four-dimensional collaborative paths: first, technology empowerment, deployment of alliance chain and zero-knowledge proof to achieve "availability and invisibility", and construction of multi-modal retrieval and personalized recommendation engine; Second, institutional innovation, the development of open value assessment guidelines and "negative list+white list" mechanism, the establishment of joint meetings and third-party performance audits; The third is management optimization, implementing the dual-track talent project of "archives +IT" and reshaping the accurate service process driven by user portraits; Fourth, social coordination, building a digital community of "urban memory" of archives, libraries and museums, and introducing the feedback mechanism of crowdsourcing and cultural and creative income. The research provides an operational framework for the government to formulate an open policy and the digital transformation of institutions, and promotes the archival resources from "physical concentration" to "value aggregation".
Blockchain technology is a Distributed Ledger Technology (DLT) where the digital information is stored across multiple computers and not centralized.Each system stores a copy of DLT to avoid single point of failure.Blockchain stores the information in blocks.All copies are validated and updated simultaneously.There are four main types of blockchain, they are Private / Permissioned, Public / Permissionless, Hybrid and Consortium.Corda is a distributed ledger open-source platform, it was introduced by R3 consortium (R3CEV LLC).It is not a public blockchain, based on agreement network and Peer to Peer (P2P) connections.No native cryptocurrency.Tech stack platform based out of JVM written in Koltin.
Abhinav Raghav, Aanjey Mani Tripathi, Niyaz Ahmad Wani, Naveed Ahmad · 6 authors
Data transactions in healthcare are steadily increasing across various platforms, aiming to improve patient care and increase data transparency. Blockchain technology will serve as a catalyst in healthcare data transactions, ensuring data security and privacy for various stakeholders. Improving data security, transparency, and interoperability, blockchain technology's application in healthcare has demonstrated considerable promise. However, healthcare applications that rely on real-time data transaction settlement face obstacles caused by Layer1 blockchains' poor transaction throughput and excessive latency. In this work, we adopt established consensus and a zk-Rollup verification workflow, specifying healthcare-oriented configurations for security, auditability, and throughput. This paper integrates the smart contracts, zero knowledge proof and off chain data storage to increase the efficiency, and security and reduce transaction costs. The usefulness of the suggested algorithm in healthcare applications is demonstrated by thorough literature research, comparative analysis, and experimental data. Transaction throughput increases very high, latency improved by 57%, and decrease the transaction cost to 96% in healthcare data transactions which are all greatly improved by the proposed system. Unlike existing zk-Rollup-based healthcare frameworks, the proposed model integrates cross-chain identity validation and verifiable data provenance to achieve secure interoperability across multi-chain healthcare systems.
As blockchain evolves toward complex Decentralized Finance (DeFi) applications, traditional serial execution becomes a primary bottleneck. Sharding offers a promising Layer-1 scaling solution but trades off cross-shard atomicity and execution latency. Existing solutions enforcing strong consistency via global sequencing or heavy two-phase commit (2PC) often suffer from high latency and resource redundancy. We propose HD-OCC, a high-performance framework combining deterministic execution with assertion-based Optimistic Concurrency Control (OCC). HD-OCC utilizes Proof of History$(\text{PoH})$for a dual-layer sequencing architecture, establishing a global partial order that decouples local processing from global consensus. Our assertion-based OCC enables the coordinator to speculatively execute transactions while parallelizing resource locking to mask network latency. Additionally, a deterministic scheduling engine implicitly constructs a deadlock-free dependency graph. Experimental results demonstrate that HD-OCC achieves linear throughput scalability and significantly lower end-to-end latency compared to monolithic architectures and deterministic systems like Calvin.
Yubin Kim, Ken Gu, Chanwoo Park, Chunjong Park · 20 authors
Abstract Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the principles that determine their performance remain underexplored, leaving practitioners to rely on heuristics rather than principled design choices. We address this gap by deriving quantitative scaling principles for agent systems. We first formalize a definition for agentic evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, model capability, and task properties. We evaluate this across four diverse benchmarks: Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench, spanning financial reasoning, web navigation, game planning, and workflow execution. Using five canonical agent architectures (Single-Agent System and four Multi-Agent Systems: Independent, Centralized, Decentralized, Hybrid), instantiated across three LLM families, we perform a controlled evaluation spanning 180 configurations, standardizing tools, prompt structures, and token budgets to isolate architectural effects from implementation confounds. We derive a predictive model using empirical coordination metrics, including efficiency, overhead, error amplification, and redundancy, that achieves cross-validated 𝑅^2=0.524, enabling prediction on unseen task domains by modeling task properties rather than overfitting to a specific dataset. We identify three dominant effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead. (2) a capability saturation: we observe that coordination yields diminishing or negative returns (𝛽=−0.404, 𝑝
Aditya Rathore, Kratika Mishra, Vidhi Chandrayan, Pareek Ch. S.
Blockchain technology has evolved into one of the most influential digital innovations of the 21st century, enabling decentralized, trustless, and tamper‑resistant data management across global networks. Its rapid rise can be attributed to groundbreaking applications across cryptocurrencies, decentralized finance (DeFi), healthcare, supply chain, and identity management systems. Despite this explosive growth, blockchain technology still faces major challenges—most critically, scalability. This extended study explores blockchain’s historical development, factors driving adoption, technical architecture, and the limitations restricting mass deployment. The paper includes an in‑depth analysis of publicly available blockchain datasets that support research in security, analytics, and scalability modeling. Furthermore, the study reviews emerging scalability frameworks such as sharding, off‑chain computation, Layer‑2 rollups, DAG-based systems, and consensus optimization. The goal is to provide a comprehensive foundation for understanding blockchain’s evolution while outlining future paths toward global-scale adoption.
This study sets forth a blockchain-driven system for safe, traceable, and effective supply chain management in manufacturing. It combines Ethereum blockchain, AI-powered fraud detection, and real-time tracing. A CNN-LSTM hybrid model was found most useful for anomaly detection, supplemented by smart contracts for automatic monitoring. The system was constructed with a React front-end, SQLite, Web3, Solidity, PyTorch, and an external API for geographical mapping. The outcomes demonstrate improvements in fraud detection, transparency, and operational efficiency, highlighting the feasibility of a scalable, tamper-evident architecture for intelligent supply chain systems.
The immense Digital Public Infrastructure (DPI) landscapes, such as Aadhaar, Digi Locker, and UPI, built by India’s rapid digitization, promote large-scale Identity, document, and finance services. However, centralised identity systems are very risky, such as a single point of failure, privacy violations, identity theft, lack of user control over their personal data. This increasing reliance on centralised frameworks underlines an acute need for more secure, private, and citizens-centric identity solutions. This research introduces a decentralized identity model that is based on both the principles of blockchain and the Self-Sovereign Identity (SSI). In the proposed system, the individuals are in control of their credentials since they use secure digital wallets to employ these credentials, while verifiable credentials are stored in an immutable blockchain network. The system relies on public-key cryptography, zero-knowledge proofs, and decentralized identifiers (DIDs) in authenticating users without revealing sensitive personal details. A layered architecture is proposed and connected to the existing government DPI platform by way of a permissioned blockchain network to support a scalable and aligned system with the decentralized identity model. Simulation parameters involved are transaction throughput, latency, resistance, and privacy leakage metrics under changing network conditions, as well as identity usage volumes. The presented algorithms for registration, verification, and identity revocation are robust, efficient, and immune to tampering of data or spoofing an identity. Simulation results validate enhanced security, privacy, scalability, and user empowerment compared to the traditional centralized systems. The bottom line is that the decentralized identity framework is not only capable of strengthening India’s DPI from cyber threats, systemic weaknesses but also guarantees that of an inclusive, user-controlled, and future-ready digital identity management system for more than a billion citizens amidst an ever-expanding digital ecosystem.
The rapid expansion of the Internet of Things (IoT) has introduced critical security challenges in authentication, data integrity, and privacy preservation. Traditional digital signature schemes, such as RSA and ECDSA, rely on identity-based trust models, which face scalability bottlenecks, lack fine-grained access control, and pose privacy risks in IoT environments. Attribute-based signatures (ABS) offer a promising solution by allowing devices to sign data only if their attributes satisfy a predefined policy, without revealing their exact identity. However, most existing ABS constructions rely on pairing-based cryptography, which is vulnerable to quantum computer attacks, while lattice-based ABS schemes often suffer from either large signature sizes or dependence on non-interactive zero-knowledge (NIZK) proofs. In this paper, we propose an efficient lattice-based ABS scheme that eliminates the need for NIZK proofs while achieving constant-size signatures. Our construction leverages the lattice-based vector commitment technique to achieve quantum resistance while reducing signature size to a constant independent of the number of attributes, significantly improving efficiency compared to prior works. Experimental evaluations confirm that our scheme outperforms existing lattice-based ABS in both computational cost and signature size, particularly for large attribute sets and deep policy circuits. Our results pave the way for practical ABS deployment in resource-constrained IoT applications, such as secure firmware updates, industrial access control, and vehicular networks.
Currently housing finance transaction platforms face challenges of data protection and cybersecurity. Blockchain technology, with its decentralization, non-tampering and high transparency, has become an effective tool for securing transaction data. In this paper, a blockchain-based data protection scheme for housing finance transaction platform is designed, which combines the shared energy storage system and realizes the cyber security protection of the transaction platform by optimizing the PBFT consensus mechanism. Methodologically, distributed file storage technology (IPFS) and smart contracts are adopted to ensure data encryption, storage and transaction transparency. Experimental results show that the proposed scheme excels in smart contract execution time, with a maximum execution time of 0.8ms, and achieves a significant increase in TPS when the concurrent volume of transactions reaches 1,200, and the throughput of the dual-chain architecture is increased by 28% compared to the traditional single-chain architecture. In addition, the system with ITPBFT consensus mechanism reduces the communication overhead by 46.19% compared to the traditional PBFT, and the consensus delay is also significantly reduced, with an efficiency improvement of 53.61%. The study shows that the proposed optimization scheme can enhance the efficiency and reliability of data transactions while improving the security of the system.
Cryptocurrency is a novel exploration of a form of currency that proposes a decentralized electronic payment scheme based on blockchain technology and cryptographic theory. While blockchain has the security characteristics of being distributed and tamper-proof, increasing market demand has led to a rise in malicious transactions and attacks, thereby exposing cryptocurrency to vulnerabilities, privacy issues, and security threats. Particularly concerning are the emerging types of attacks and threats, which have made securing cryptocurrency increasingly urgent. This paper classifies existing cryptocurrency security threats and attacks into five fundamental categories based on the blockchain infrastructure, and it analyzes in detail the vulnerability principles exploited by each type of threat and attack. Furthermore, the paper examines the attackers’ logic and methods and provides insights that enable easy reproduction of the vulnerabilities. We also summarize and evaluate existing detection and defense solutions, offering important references for ensuring cryptocurrency security. Finally, the paper discusses the future development trends of cryptocurrency.
Abstract The emerging growth of Quantum computing has significant challenges to change the classical cryptographic protocols, the security of AI–Blockchain systems to provide long-term security and provide federated learning (FL) for current cryptographic systems. This paper introduces a new framework as Quantum-Resistant Federated AI on Blockchain (QFAIB) that integrates Post-Quantum Cryptography (PQC) algorithms with real-time Federated AI Learning (FAIL) on distributed ledgers creation. The proposed QFAIB ensures end-to-end data security and confidentiality, decentralized trust, and adaptive intelligence, resistant to quantum decryption attacks. Through the integration of hybridization of CRYSTALS-Kyber encryption to create challenging task, that makes the Dilithium digital signatures, and Zero-Knowledge Proofs (ZKP) for privacy-preserving model validation and decentralized federated AI models, the proposed QFAIB compared with baseline and evaluated as the performance metrics as data integrity verification accuracy (DIVA), auditing efficiency (AE), quantum resistance efficiency (QRE), privacy leakage reduction (PLR) and throughput (TT) in multi-cloud and IoT environments and this work proved that real time distributed ledger creations.
SECTION IV — E-Coin Technical Design & Architecture E-Coin is not a currency, but an Operating System for civilization. This section describes the technical and architectural design of E-Coin as a civilizational operating system that separates, yet co-evolves, value, cognition, and agency. E-Coin adopts a three-layer architecture composed of a Distributed Ledger Layer (Value Foundation), an AI Cognitive Layer (Reason Engine), and a Human Interface Layer (Mind-OS). This separation prevents the concentration of power while enabling interoperability between human decision-making, AI inference, and value exchange. The design explicitly prohibits AI systems from overriding human agency, positioning AI instead as a cognitive collaborator and translator. At the foundation, the Distributed Ledger Layer employs zero-knowledge proofs, decentralized identifiers, and post-quantum cryptography to ensure security, privacy, and human rights by default. Data ownership remains with individuals at all times, supported by built-in rights to deletion, anonymization, and refusal of access. Unlike conventional cryptocurrencies or CBDCs, this layer is consent-based and cognition-centered rather than economy-centric. The AI Cognitive Layer functions as a civilization-wide reasoning substrate. It includes alignment cores, non-numerical cognitive reputation indices, adaptive governance agents, and layered memory management across individual, collective, and civilizational scales. While AI systems may negotiate and coordinate at this layer, decision authority is structurally constrained to remain human-centered. The Human Interface Layer (Mind-OS) focuses on the expansion of human consciousness rather than dependency or control. It includes mechanisms for cognitive load scaling, consciousness mode switching, and protection against emotional inducement or manipulation. Together, these layers form an evolvable, future-proof architecture designed to remain stable as both AI capabilities and civilization itself continue to evolve. E-Coin does not replace existing systems but integrates with Web3, AI/AGI, smart cities, and emerging technological domains through synthesis rather than disruption. Keywords E-Coin, civilizational OS, AI architecture, human-AI interface, distributed systems, ethical AI
Contemporary enterprises encounter substantial difficulties managing information dispersed across varied cloud infrastructures, geographically separated facilities, and specialized application environments. Traditional centralized frameworks, including consolidated data repositories and analytical warehouses, demonstrate limited capacity to deliver the required velocity, accuracy, and contextual intelligence necessary for sustained digital progression. Multi-Cloud Data Mesh constitutes a transformative architectural approach, advocating decentralized, domain-centric methodologies that systematically address intricate governance complexities and interoperability obstacles at the organizational scale. This framework establishes operational foundations through four fundamental tenets: Domain-Oriented Ownership, Data as a Product, Self-Serve Platform, and Federated Computational Governance. These architectural pillars collectively resolve decentralization imperatives, scalability prerequisites, interoperability complications, and sovereignty considerations inherent in modern enterprise ecosystems. Through ownership distribution to specialized domains, product-oriented information treatment, self-service platform provisioning, and federated governance implementation, organizations attain necessary scalability, operational flexibility, and contextual precision for continuous innovation across sophisticated multi-cloud landscapes
Matteo Loporchio, Damiano Di Francesco Maesa, Anna Bernasconi, Laura Ricci
Abstract The ERC-1155 standard introduced on the Ethereum blockchain allows for managing multiple tokens, both fungible and non-fungible, within a single contract. It also supports batch transfers, thereby reducing transaction costs and enabling a more efficient use of blockchain resources. To assess its impact and level of adoption, this paper presents a comprehensive analysis of the ERC-1155 token ecosystem. First, we examine the activity of ERC-1155 contracts and compare the evolution of transfer volumes with those of the two alternative most popular token management standards. Next, we model the economy of each ERC-1155 contract as a directed graph, where nodes represent users and edges denote token transfers. We then study the topological properties of such graphs, analyzing approximately 40,000 networks until the end of 2024. Results indicate that, within our dataset, the adoption of ERC-1155 is growing, although its functionalities are not being fully utilized. Additionally, about 60% of the networks exhibit a completely centralized topology, while the remaining ones are generally sparse and lack small-world characteristics. Finally, the degree distribution analysis shows that preferential attachment is only present in a minority of the networks and the graphs also display a mild disassortative behavior.
Journal of Theoretical and Applied Information Technology
With the growing volume of health information it has become common practice to protect the patient identity while maintaining convenient access to the data. Due to varying flow of cyber security threats, traditional solutions never manage to get flexible access to data without compromising with overflow of data. To overcome these challenges focusing on patient data protection, in this paper, we propose a new Hybrid Integrated Hashing approach entitled "Dynamic Adaptive Hash-Block Access Control (DAHBAC) framework" using blockchain based advanced data access control mechanism. The dynamic multi factor hashing scheme can change in response to the current Vulnerability of data and access patterns, whereas data access control refers to leverage blockchain's immutability and decentralized structure that helps protecting patient privacy while allowing authorized persons to read. The dynamic hashing method prevents intruder attempts by making hash and easy to calculate but requiring real-time modification of the hash for access protection. This is made possible by harnessing the application of zero-knowledge proofs (ZKP) within the frame of blockchain to enable verification of information when there is no disclosure of the data. Compared with the conventional methods, testing of prototype in a health care organization resulted in 92% on attempts by unauthorized workers to enter the system and 7% increasing data retrieval rate. These findings shows that the proposed model is a perfect patient data protection pattern in ehealth systems, because it is not only secures patients data but also enhances the accessibility and scalability to handle more clients. It is enabled by the use of zero-knowledge proofs (ZKP) in combination with blockchain technology to verify information, while keeping the information secret.
Smart contract technology facilitates self-executing agreements on the blockchain, eliminating dependency on an external trusted authority. However, smart contracts may expose vulnerabilities that can lead to financial losses and disruptions in decentralized applications. In this work, we evaluate deep learning-based approaches for vulnerability scanning of Ethereum smart contracts. We propose VASCOT, a Vulnerability Analyzer for Smart COntracts using Transformers, which performs sequential analysis of Ethereum Virtual Machine (EVM) bytecode and incorporates a sliding window mechanism to overcome input length constraints. To assess VASCOT's detection efficacy, we construct a dataset of 16,469 verified Ethereum contracts deployed in 2022, and annotate it using trace analysis with concrete validation to mitigate false positives. VASCOT's performance is then compared against a state-of-the-art LSTM-based vulnerability detection model on both our dataset and an older public dataset. Our findings highlight the strengths and limitations of each model, providing insights into their detection capabilities and generalizability.
Étudier le Bitcoin avec des méthodes de sciences sociales computationnelles Cette thèse étudie l'émergence, l'évolution et les dynamiques internes du Bitcoin en tant que système sociotechnique et économique. Conçu initialement comme une alternative radicale à la finance traditionnelle, le Bitcoin visait à décentraliser le système économique, à éliminer les intermédiaires et à favoriser l'autonomie. Pourtant, plutôt que d'instaurer un nouvel ordre financier, des structures et des modèles similaires à ceux des marchés traditionnels se sont rapidement constitués. Cette similarité croissante soulève donc des questions fondamentales sur les mécanismes qui régissent la trajectoire du Bitcoin, sa pérennité et ses implications pour la finance mondiale. S'appuyant sur un jeu de données répertoriant quinze années de transactions, cette thèse combine analyse de réseau, modélisation temporelle de réseau et modélisation multi-agents pour saisir la complexité du Bitcoin. L'analyse montre que, malgré des fondements idéologiques profondément enracinés dans la décentralisation, le Bitcoin a évolué vers un réseau hautement centralisé et concentré. La richesse, l'activité et l'influence s'accumulent de plus en plus au sein d'un petit groupe d'acteurs, créant des goulots d'étranglement et des dynamiques de stabilisation qui ressemblent aux structures financières traditionnelles. La thèse explore plus en détail les réactions du Bitcoin à l'incertitude et aux chocs exogènes, notamment lors de l'effondrement de Mt. Gox en 2014 et de la pandémie de COVID-19 en 2020. Les résultats indiquent que, si le système s'adapte par la reconfiguration du réseau, ses réponses sont asymétriques : la crise de Mt. Gox a engendré des changements structurels durables, tandis que la pandémie a déclenché des ajustements rapides mais temporaires. Ces derniers ressemblent aux schémas de reconfiguration observés sur les marchés traditionnels et révèlent l'influence croissante de la participation institutionnelle, qui amplifie la volatilité à court terme tout en renforçant la stabilité à long terme. Enfin, un modèle multi-agents calibré empiriquement du trading de Bitcoin démontre comment les comportements au niveau micro, en combinant les préférences de prix et de réseau, reproduisent les tendances de concentration et de centralisation au niveau macro. Ce modèle met en évidence l'influence des interactions en réseau et de la dynamique comportementale sur les asymétries structurelles du système. Dans l'ensemble, la thèse révèle de fortes similitudes entre le Bitcoin et les marchés financiers traditionnels, remettant en question les discours sur la décentralisation et l'autonomie radicale de cette cryptomonnaie. Loin de nourrir une utopie libertaire, le Bitcoin a convergé vers des logiques de marché familières, soulevant des questions sur sa viabilité à long terme, ses risques systémiques et son intégration réglementaire.